Distributed Causality in the SDG Network: Evidence from Panel VAR and Conditional Independence Analysis

MMM Fahim, MJH Imran, L Debnath, T Shill, MN Molla, EB Pranto, MSS Saad, MR Karim

arXiv preprint · 2026 · preprint · arXiv:2601.20875

Abstract

Achievement of the 2030 Sustainable Development Goals depends on strategic resource distribution. We propose a causal discovery framework using Panel Vector Autoregression with country-specific fixed effects and PCMCI+ conditional independence testing on 168 countries (2000–2025) to develop the first complete causal architecture of SDG dependencies. Analyzing 8 strategically chosen SDGs, we identify a distributed causal network (no single 'hub' SDG) with 10 statistically significant Granger-causal relationships as 11 unique direct effects. Education to Inequality is the most statistically significant direct relationship (r = −0.599; p < 0.05), with effect magnitude varying substantially by income level (high-income: r = −0.65; lower-middle-income: r = −0.06, non-significant). We propose a tiered priority framework identifying upstream drivers (Education, Growth), enabling goals (Institutions, Energy), and downstream outcomes (Poverty, Health), concluding that effective SDG acceleration requires coordinated multi-dimensional interventions rather than single-goal sequential strategies.

BibTeX

@article{fahim2026distributed,
  title   = {Distributed Causality in the SDG Network: Evidence from Panel VAR and Conditional Independence Analysis},
  author  = {MMM Fahim and MJH Imran and L Debnath and T Shill and MN Molla and EB Pranto and MSS Saad and MR Karim},
  year    = {2026},
  journal = {arXiv preprint},
  eprint  = {2601.20875},
  archivePrefix = {arXiv},
  url     = {https://arxiv.org/abs/2601.20875},
}